Kişilerin Sosyal ve Fiziksel Aktivitelerine Göre Obezite Durumunun Analizi için Yapay Zeka Tekniklerinin Kullanımı

Yıl 2024, , 217 - 239, 29.06.2024

Nigmet Koklu , Süleyman Alpaslan Sulak

https://doi.org/10.33484/sinopfbd.1445215

Öz

Obezite, genetik ve çevresel etkileşimlere sahip ciddi ve kronik bir hastalıktır. Sağlığa zararlı olan vücuttaki aşırı miktardaki yağ dokusu olarak tanımlanır. Obezitenin başlıca risk faktörleri, sosyal, psikolojik ve beslenme alışkanlıklarını içerir. Obezite, dünya genelinde tüm yaş grupları için önemli bir sağlık sorunudur. Şu anda dünya genelinde 2 milyardan fazla insan obez veya aşırı kilolu durumdadır. Araştırmalar, obezitenin önlenebileceğini göstermektedir. Bu çalışmada, obezite riski taşıyan bireyleri tanımlamak için yapay zeka yöntemleri kullanıldı. Obezite veri setini oluşturmak için 1610 birey üzerinde çevrimiçi bir anket yapıldı. Anket verilerini analiz etmek için literatürde yaygın olarak kullanılan dört yapay zeka yöntemi olan Yapay Sinir Ağı, K En Yakın Komşu, Rastgele Orman ve Destek Vektör Makinesi, kullanıldı. Bu analizin sonucunda, obezite sınıfları sırasıyla %74.96, %74.03, %74.03 ve %87.82 başarı oranlarıyla doğru bir şekilde tahmin edildi. Rastgele Orman, bu veri seti için en başarılı yapay zeka yöntemi oldu ve obeziteyi %87.82 başarı oranıyla doğru bir şekilde sınıflandırdı.

Anahtar Kelimeler

Obezite veri seti, yapay zeka yöntemleri, yapay sinir ağı, destek vektör makinesi, k- en yakın komşu, rastgele orman

Using Artificial Intelligence Techniques for the Analysis of Obesity Status According to the Individuals' Social and Physical Activities

Öz

Obesity is a serious and chronic disease with genetic and environmental interactions. It is defined as an excessive amount of fat tissue in the body that is harmful to health. The main risk factors for obesity include social, psychological, and eating habits. Obesity is a significant health problem for all age groups in the world. Currently, more than 2 billion people worldwide are obese or overweight. Research has shown that obesity can be prevented. In this study, artificial intelligence methods were used to identify individuals at risk of obesity. An online survey was conducted on 1610 individuals to create the obesity dataset. To analyze the survey data, four commonly used artificial intelligence methods in literature, namely Artificial Neural Network, K Nearest Neighbors, Random Forest and Support Vector Machine, were employed after pre-processing. As a result of this analysis, obesity classes were predicted correctly with success rates of 74.96%, 74.03%, 74.03% and 87.82%, respectively. Random Forest was the most successful artificial intelligence method for this dataset and accurately classified obesity with a success rate of 87.82%.

Anahtar Kelimeler

Obesity dataset, artificial intelligence methods, artificial neural network, support vector machine, k nearest neighbors, random forest

Etik Beyan

The ethics committee document of the research was received with decision number 2023/201 at the meeting numbered 06 of Necmettin Erbakan University Social and Human Sciences Scientific Research Ethics Committee dated 12/05/2023

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